Search Results for "raw"

BioXTAS RAW is a program for analysis of Small-Angle X-ray Scattering (SAXS) data. The software enables: creation of 1D scattering profiles from 2D detector images, standard data operations such as averaging and subtraction, analysis of radius of gyration (Rg) and molecular weight, and advanced analysis using GNOM and DAMMIF as well as electron density reconstructions using DENSS.

The Sashimi project hosts the Trans-Proteomic Pipeline (TPP), a mature suite of tools for mass-spec (MS, MS/MS) based proteomics: statistical validation, quantitation, visualization, and converters from raw MS data to the open mzML/mzXML formats.

Converts RAW Thermo Files into MGF files (Python 3 version). For more information, you can have a look at the README.md file in the source code tree: https://sourceforge.net/p/lp-csic-uab/easiermgf3/code/ci/default/tree/README.md - Gallardo, Ó., Ovelleiro, D., Gay, M., Carrascal, M., & Abian, J. (2014). A collection of open source applications for mass spectrometry data mining.

PPLine is a Python-based suite aimed to process raw RNA-seq or Exome-seq data. PPLine provides: - read mapping (STAR/Tophat2/bowtie/bowtie2), including novel splice junsctions discovery - gene and transcript expression estimation (HTSeq-count/Cufflinks) - SNP calling with BQSR and indel realignment (samtools/GATK) - variant annotation (Annovar) - novel transcripts discovery (Cufflinks) - predicting proteotypic peptides and creating ref/alt proteins fasta-database - integration of the results

pyQPCR is a GUI application written in python that deals with quantitative PCR (QPCR) raw data. Using quantification cycle values extracted from QPCR instruments, it uses a proven and universally applicable model to give finalized quantification resu

This script can convert raw diffraction images to JPEG files. Support ADSC, MarCCD, Mar345, RAXIS and PILATUS, and can be run on Windows, Linux and Mac OS X.

mitoMaker is a pipeline script developed to simplify the assembly and automatic annotation of mitochondrial genomes, based on raw NGS reads and an optional target reference. mitoMaker calls well known assemblers and algorithms, such as SOAPdenovo, MIRA and blast+ and parses their results providing easily readable outputs, such as FASTA, GENBANK, SEQUIN, PNG and others. General pipeline: 1-iterative De Novo assembly, with different k-mer values, trying to assemble a build that matches a target mitochondrial genome given. 2-searches for all mitochondrial gene features and circularization. 3-stores the best result found. 4-uses the best assembly as backbone for a reference based assembly, using MIRA and MITObim, trying to extend the mitogenome and close gaps. 5-annotates the best assembly, identifying the start and end position of each and every feature. 6-creates a folder with all the results (PNG, GENBANK, FASTA, SEQUIN, CAF, MAF and a stats logfile).

ScreenSifter can help you in managing all your RNAi screens related projects in organized fashion. ScreenSifter, to perform the sequential, user friendly, guided, and advanced statistical analyses of RNAi screening results. User can upload their raw signal intensities and will avail complete quality control of screen, hit selection, plotting of hit genes with gene ontology, comparing of replicates or comparing of channels. In addition, ScreenSifter has visualization tools to plot specific genes or gene groups in the screen data. ScreenSifter also provides Gene Set Enrichment Analysis (GSEA), protein-protein interaction directly on the plot. ...

ReadWrapper is a Python toolset to prepare and package raw sequencing data ("reads") for submission to the Sequence Read Archive (SRA, http://trace.ncbi.nlm.nih.gov/Traces/sra/).

This is an automatic spike detection program which takes account of probe geometry and produces a .mask file to be used with the new masked version of KlustaKwik. We recommend you use Python 2.6 or 2.7, e.g. a free academic version can be obtained from Entthought Python. The input files for SpiKeDeteKt are: .dat (raw data file) .probe (probe file, described below - user constructed) parameters.py (optional - otherwise it uses defaultparameters.py) SpiKeDeteKt outputs the following files: .fet.n (feature file) .mask.n (needed for using the new (masked) KlustaKwik) .clu.n (a trivial clue file where everything is put into a single cluster) .fmask.n (trial - float masks instead of binary, we are using this for testing masked KlustaKwik) .spk.n (spike file) .upsk.n (unfiltered spike waveform) .res.n (list of spike times) .xml (an xml file with all the parameters that can subsequently be used by neuroscope or klusters) .fil (highpass filtered data) .h5 (

The aim of this project is to create an automated pipeline for taxonomic assignment of DNA sequences obtained from environmental samples. We develop a series of python scripts to process the raw sequence data obtained from benthic environmental samples and to taxonomical assignment of these sequences and finally to integrate all data in a relational database.

SureFit generates 3D mesh surfaces (in VTK and Caret formats) from 3D structural MRI volumes (in formats such as Analyze, AFNI, raw).